Automated feature generation, selection and hyperparameter tuning from structured data for supervised learning problems

Abstract

Methods and systems for automating supervised learning tasks are provided. Feature generation in a feature space having a plurality of features using at least one predefined process for a plurality of data types is performed. A minimum set of relevant features are identified. The feature space is decreased using at least one filtering approach and the minimum set of relevant features. A Bayesian combinatorial optimization heuristic is devised to jointly identify a feature subset and a hyperparameter setting for a given query, a machine learning algorithm, and a dataset.

What is claimed is: 
     
         1 . A method for automating supervised learning tasks comprising:
 performing feature generation in a feature space having a plurality of features using at least one predefined process for a plurality of data types;   identifying a minimum set of relevant features;   decreasing the feature space using at least one filtering approach and the minimum set of relevant features; and   devising a Bayesian combinatorial optimization heuristic to jointly identify a feature subset and a hyperparameter setting for a given query, a machine learning algorithm, and a dataset.   
     
     
         2 . The method of  claim 1  further comprising removing features using at least one of near zero variance, correlation analysis, lasso, and random forest. 
     
     
         3 . The method of  claim 1  wherein identifying a minimum set of relevant features further comprises removing two features from the feature space. 
     
     
         4 . The method of  claim 1  further comprising ranking features by importance. 
     
     
         5 . The method of  claim 1  further comprising evaluating a generalization error for the hyperparameter setting. 
     
     
         6 . The method of  claim 5  further comprising determining whether the generalization error has increased monotonically over a given number of iterations. 
     
     
         7 . The method of  claim 1  further comprising recovering a relevant feature from a set of non-selected features. 
     
     
         8 . The method of  claim 1  further comprising performing preprocessing on the dataset. 
     
     
         9 . The method of  claim 1  further comprising outputting the Bayesian combinatorial optimization heuristic. 
     
     
         10 . The method of  claim 1  further comprising refining the Bayesian combinatorial optimization heuristic by assessing it with the dataset. 
     
     
         11 . A configuration system comprising one or more processors which, alone or in combination, are configured to provide for performance of the following steps:
 performing feature generation in a feature space having a plurality of features using at least one predefined process for a plurality of data types;   identifying a minimum set of relevant features;   decreasing the feature space using at least one filtering approach and the minimum set of relevant features; and   devising a Bayesian combinatorial optimization heuristic to jointly identify a feature subset and a hyperparameter setting for a given query, a machine learning algorithm, and a dataset.   
     
     
         12 . The system of  claim 11  further comprising removing features using at least one of near zero variance, correlation analysis, lasso, and random forest. 
     
     
         13 . The system of  claim 11  wherein identifying a minimum set of relevant features further comprises removing two features from the feature space. 
     
     
         14 . The system of  claim 11  further comprising ranking features by importance. 
     
     
         15 . The system of  claim 11  further comprising evaluating a generalization error for the hyperparameter setting.